{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 32, 32, 3) (500, 32, 32, 3)\n",
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "print(X_train.shape, X_test.shape)  # original\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting future\n",
      "Installing collected packages: future\n",
      "Successfully installed future-0.17.1\n"
     ]
    }
   ],
   "source": [
    "!pip install future  # to use past.builtin module for python 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: \n",
    "\n",
    "- The bright rows indicates some test images that are very different from most of the train images\n",
    "- The bright columns means that some train images are very different from most of the test images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 37.079521 seconds\n",
      "One loop version took 99.282248 seconds\n",
      "(500, 5000) (5000,) (500, 1)\n",
      "No loop version took 2.649877 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.237000\n",
      "k = 3, accuracy = 0.251000\n",
      "k = 3, accuracy = 0.246000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.261000\n",
      "k = 5, accuracy = 0.243000\n",
      "k = 5, accuracy = 0.278000\n",
      "k = 5, accuracy = 0.273000\n",
      "k = 5, accuracy = 0.297000\n",
      "k = 5, accuracy = 0.278000\n",
      "k = 8, accuracy = 0.257000\n",
      "k = 8, accuracy = 0.293000\n",
      "k = 8, accuracy = 0.272000\n",
      "k = 8, accuracy = 0.285000\n",
      "k = 8, accuracy = 0.270000\n",
      "k = 10, accuracy = 0.270000\n",
      "k = 10, accuracy = 0.298000\n",
      "k = 10, accuracy = 0.279000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 10, accuracy = 0.288000\n",
      "k = 12, accuracy = 0.255000\n",
      "k = 12, accuracy = 0.291000\n",
      "k = 12, accuracy = 0.278000\n",
      "k = 12, accuracy = 0.275000\n",
      "k = 12, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.266000\n",
      "k = 15, accuracy = 0.284000\n",
      "k = 15, accuracy = 0.281000\n",
      "k = 15, accuracy = 0.279000\n",
      "k = 15, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.269000\n",
      "k = 20, accuracy = 0.278000\n",
      "k = 20, accuracy = 0.280000\n",
      "k = 20, accuracy = 0.277000\n",
      "k = 20, accuracy = 0.281000\n",
      "k = 50, accuracy = 0.272000\n",
      "k = 50, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.281000\n",
      "k = 50, accuracy = 0.265000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.260000\n",
      "k = 100, accuracy = 0.272000\n",
      "k = 100, accuracy = 0.261000\n",
      "k = 100, accuracy = 0.255000\n",
      "k = 100, accuracy = 0.261000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.split(X_train, num_folds)\n",
    "y_train_folds = np.split(y_train, num_folds)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "  accuracy_list_for_k = []\n",
    "  for f in range(num_folds):  # f=0,1,2,3,4\n",
    "    classifier = KNearestNeighbor()\n",
    "\n",
    "    X_test_k = X_train_folds[f]  # use 1/k samples for test\n",
    "    y_test_k = y_train_folds[f]\n",
    "\n",
    "    X_train_k = np.array([]).reshape(-1, 3072)  # match the length to concatenate\n",
    "    y_train_k = np.array([])  # y is simple 1-d array\n",
    "    for _f in range(num_folds):\n",
    "      if _f != f:\n",
    "        X_train_k = np.concatenate((X_train_k, X_train_folds[_f]), axis=0)  # use 1-1/k samples for training\n",
    "        y_train_k = np.concatenate((y_train_k, y_train_folds[_f]), axis=0)\n",
    "\n",
    "    classifier.train(X_train_k, y_train_k)\n",
    "    dists = classifier.compute_distances_no_loops(X_test)  # compute distances\n",
    "    y_test_pred = classifier.predict_labels(dists, k)\n",
    "\n",
    "    num_correct = np.sum(y_test_pred == y_test)\n",
    "    accuracy = float(num_correct) / len(y_test)\n",
    "    accuracy_list_for_k.append(accuracy)\n",
    "  k_to_accuracies[k] = accuracy_list_for_k\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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